A Robust Unsupervised Domain Adaptation Framework for Medical Image Classification Using RKHS-MMD

arXiv cs.CV / 5/6/2026

📰 NewsIdeas & Deep AnalysisModels & Research

共有:

Key Points

Medical image classification suffers from limited generalization because labeling is expensive and domain shifts arise from differences in medical centers and imaging devices.
The paper proposes an unsupervised domain adaptation framework that aligns source and target distributions using an RKHS-MMD loss combined with transfer learning.
Training jointly optimizes a classification objective and the RKHS-MMD alignment term to improve performance on unannotated medical datasets.
Experiments on two chest X-ray datasets from different centers show substantial gains versus models trained without domain adaptation.
A comparison indicates RKHS-MMD reduces the modality gap more effectively than standard MMD, supporting its value for medical diagnostic AI.

Abstract

Labeling medical images is a major bottleneck in the field of medical imaging, as it requires domain-specific expertise, and it gets further complicated due to variability across different medical centers and different imaging devices. Such heterogeneity introduces domain shifts and modality discrepancies, which limits the generalization of trained models. To address this important challenge, we propose an unsupervised domain adaptation framework that combines transfer learning with a Reproducing Kernel Hilbert Space based Maximum Mean Discrepancy loss for the alignment of source and target domains. By jointly optimizing classification and RKHS-MMD losses, the methodology enhances generalization to unannotated medical datasets while diminishing reliance on manual annotation. Experimental evaluations presented on two chest X-ray datasets, which are obtained from different medical centers, show outstanding improvements over models trained without adaptation. Furthermore, we perform a comparative study to see that RKHS-MMD performs better than the standard Maximum Mean Discrepancy in reducing modality gap, emphasizing its effectiveness for medical image classification and also its strong capability in advanced AI-driven medical diagnostics.